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Zero3 split model parameters across all ranks and do forward/backward computing by gather parameters from other ranks.
Also, due to the problem of gradient accumulation, zero3 is not compatable with pipeline parallel.
There are often thounds of GPUs in situation of large scale pretraining, zero3 must gather params from all gpus, it will take huge network cost although zero++ reduces communication.
From above(if I'm not wrong), I think [3D parallel + Zero 1] is still a sota solution for pretrain work, and Zero3/Zero++ is much better on SFT\RHLF.
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Hi,
Zero3 split model parameters across all ranks and do forward/backward computing by gather parameters from other ranks.
Also, due to the problem of gradient accumulation, zero3 is not compatable with pipeline parallel.
There are often thounds of GPUs in situation of large scale pretraining, zero3 must gather params from all gpus, it will take huge network cost although zero++ reduces communication.
From above(if I'm not wrong), I think [3D parallel + Zero 1] is still a sota solution for pretrain work, and Zero3/Zero++ is much better on SFT\RHLF.
So, is my understanding correct?
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